Joint Order Selection, Allocation, Batching and Picking

This project contains accompanying code for Zalando Team BART's (Batching Algorithms) publication Joint Order Selection, Allocation, Batching and Picking for Large Scale Warehouses. The instances used to create results in the paper are stored in instances.zip.

How to use

Clone this project and copy the files into your own project
Generate batching instances via python generate_instances.py (any Python 3.6+ works). For each generated instance (per default in the instances folder) four files will be created: {articles, orders, parameters, warehouse_items}.json.
Run the Distance Greedy Algorithm (DGA) via python solve_instances.py. Two files will be stored per instance under instances/<instance>/dga/: batches.json and statistics.json. The runtime in statistics.json covers the algorithm only, not reading the instance from disk.
Run the Randomized DGA via python solve_instances.py -a rdga, which writes to instances/<instance>/rdga/
Write your own solver to outperform these baseline algorithms :)

Run the tests with python -m unittest discover.

Please read CONTRIBUTING.md for details on our process for submitting pull requests to us, and please ensure you follow the CODE_OF_CONDUCT.md.

This project is licensed under the MIT License - see the LICENSE.md file for details